Open-source alternatives to SENet

YOLOv5 is a comprehensive computer vision framework designed for end-to-end deep learning, specializing in real-time object detection, image classification, and instance segmentation. It provides a unified toolkit that manages the entire lifecycle of a model, from initial dataset configuration and hyperparameter tuning to high-speed inference and deployment. The framework utilizes a modular neural architecture, allowing users to swap backbone and head components to tailor models for specific visual tasks. What distinguishes this project is its focus on production-ready deployment and model ef

Implementation of a classification framework from the paper Aggregated Residual Transformations for Deep Neural Networks

Deep Residual Networks with 1K Layers

The implementation of an algorithm presented in the CVPR18 paper: "Detect-and-Track: Efficient Pose Estimation in Videos"

This repository provides a collection of reference implementations, toolkits, and orchestration tools for training and deploying large-scale AI models on Cloud TPU hardware. It serves as a framework for managing the lifecycle of accelerator clusters, including hardware orchestration and the provisioning of high-performance compute infrastructure for machine learning workloads. The project specifically enables the pre-training of foundation models, large language models, and complex reasoning architectures through distributed training toolkits and multi-host scaling recipes. It further provide

Split-Attention Network, A New ResNet Variant. It significantly boosts the performance of downstream models such as Mask R-CNN, Cascade R-CNN and DeepLabV3.

DenseNet is a computer vision model and convolutional neural network implementation designed for image recognition and classification tasks. It utilizes a densely connected network architecture where each layer is connected to every other layer to improve feature propagation. The implementation reduces the number of parameters while maintaining accuracy through a dense-connectivity pattern and layer-aggregation concatenation. It supports model construction using both standard and bottleneck-compressed architectures, with configurable network depth and growth rates to balance inference time an

3.8% and 18.3% on CIFAR-10 and CIFAR-100

tensorrtx is a computer vision inference engine and model implementation library designed for graphics processor acceleration. It provides a framework for optimizing deep learning models through a GPU inference optimizer, a deep learning model converter for transforming weights from frameworks like TensorFlow and PyTorch, and a custom plugin library to implement operations not natively supported by the TensorRT API. The project distinguishes itself through a comprehensive collection of pre-defined network implementations, ranging from various YOLO versions and DETR transformers for object det

This project is a collection of deep learning tools for image classification and audio tagging, providing a repository of pre-trained model weights and architectures. It serves as a Keras model zoo that enables the immediate use of established neural networks for inference and transfer learning. The library includes a music tagging framework that classifies audio recordings using convolutional recurrent neural networks and mel-spectrograms. For visual data, it provides implementations of architectures such as ResNet, VGG, and Xception, alongside a repository of weights trained on large datase

Build fully-functioning computer vision models with PyTorch

This repository contains the TensorFlow code for our NeurIPS 2018 paper “Unsupervised Attention-guided Image-to-Image Translation”. This code is based on the TensorFlow implementation of CycleGAN provided by Harry Yang. You may need to train several times as the quality of the results are…

Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018

Project page for End-to-end Recovery of Human Shape and Pose

Angjoo Kanazawa \ , Shubham Tulsiani \ , Alexei A. Efros, Jitendra Malik

High-level batteries-included neural network training library for Pytorch

This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a facial landmark detector and reconstruction tool, utilizing deep learning to identify precise geometric points on human faces from image datasets. The library allows for the selection of specific detection backends to balance accuracy and processing speed. It supports the integration of precomputed bounding box files, which enables the system to bypass the initial detection phase and proceed directly to landmark extraction. The toolkit includes capabilities for batch image p

An all-in-one toolkit for computer vision

3D Computer Vision Framework

Meshroom is a node-based photogrammetry software designed to transform collections of two-dimensional images into three-dimensional models and scene geometry. It provides a visual interface for constructing and managing modular data pipelines, allowing users to automate complex computer vision tasks such as feature extraction, depth map estimation, and mesh generation. The software distinguishes itself through a distributed computational framework that dispatches resource-intensive tasks across local hardware or remote render farms. By utilizing a directed acyclic graph execution model, it en

FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) Dataset

Contains implementation of Guided Attention Inference Network (GAIN) presented in Tell Me Where to Look(CVPR 2018). This repository aims to apply GAIN on fcn8 architecture used for segmentation.

The project is an official implement of our CVPR2018 paper "Deep Back-Projection Networks for Super-Resolution" (Winner of NTIRE2018 and PIRM2018)

This is a PyTorch object detection framework that implements the Single Shot MultiBox Detector for identifying and localizing multiple objects within images and video. The project provides a neural network architecture designed for single-shot object detection, which predicts bounding boxes and class labels in one pass. The implementation includes a real-time object detector capable of processing live video streams to track and label objects across sequential frames. It also features a complete computer vision training pipeline for preparing image datasets and training model weights. The fra

Attention-based Deep Multiple Instance Learning